基于KAZE的自适应模糊图像配准算法

Adaptive registration algorithm of blurred image based on KAZE

为了提高图像配准算法对于模糊图像的配准性能,提出一种融合非线性尺度空间和空间余弦相似度的自适应模糊图像配准算法。该算法将非线性尺度空间理论应用于图像的局部特征提取,采用KAZE算法提取图像的特征点,以构成M-SURF特征描述符;利用空间余弦对图像特征点进行匹配,并且根据不同的图像特性进行自适应阈值匹配,以得到便于寻求最优变换关系的合理数量的匹配点对;最后采用RANSAC算法滤除误匹配点对,以提升算法精度。实验结果表明,该算法可以有效地提高模糊图像配准的匹配准确率和精度,准确率和精度比KAZE算法最大可以提高25%和7.909像素,具有更好的配准性能。

In order to improve the blurred image registration properties of image registration algorithm, an adaptive blurred image registration algorithm fused nonlinear scale space and space cosine similarity is proposed. The algorithm adopts the nonlinear scale space theory to extract the local feature of the image, and the feature points of the image are extracted by the KAZE algorithm to form the M- SURF feature descriptor. Using the space cosine to match the feature points, and match the adaptive threshold according to the different characteristics of the image to seek the optimal transformation relations of reasonable quantity of matching points. Finally, the RANSAC algorithm is used for excluding mistake matching points to improve the accuracy. The experimental results show that the algorithm can effectively improve the matching accuracy and precision of the fuzzy image registration, the matching accuracy and precision can he improved up to 25% and 7. 909 pixels, and has better registration performance.